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Duration 7 hours
Course Outline
Best Practices and Tools
Common Pitfalls and Mitigation Strategies
Introduction to Prompt Engineering
Prompt Refinement and Iterative Design
Prompting for Test Automation and SQL Generation
Summary and Next Steps
Using Prompts for Code Explanation and Debugging
Writing Prompts for Code Generation
- Preventing hallucinated code or security vulnerabilities.
- Managing incomplete or ambiguous inputs.
- Developing safe fallback prompts and guardrails.
- Deriving test cases from requirements or existing code.
- Creating structured SQL queries from natural language descriptions.
- Structuring outputs for seamless integration into test suites.
- Clarifying legacy or unfamiliar code segments.
- Prompting for logic explanations or edge case analysis.
- Identifying and explaining bugs or inefficiencies.
- Generating code from plain-language descriptions.
- Controlling output formats and target programming languages.
- Handling complex logic or multiple functions.
- Enhancing outcomes through prompt chaining and feedback loops.
- Error recovery and prompt tuning strategies.
- Case studies focusing on refinement for technical tasks.
- Prompt libraries and reuse patterns.
- Utilizing prompt templates in VS Code or API-based workflows.
- Assessing prompt quality and performance in production environments.
- Understanding prompts, context, tokens, and models.
- Prompt types: zero-shot, one-shot, few-shot.
- Using system vs. user instructions across different APIs.
Requirements
Target Audience
- Developers utilizing LLMs for code generation or analysis.
- Technical leads investigating AI tools within their processes.
- Software professionals exploring LLM integrations.
- Background in software development or scripting.
- Proficiency in standard programming languages (e.g., Python, JavaScript, SQL).
- Foundational knowledge of large language models and AI tools such as ChatGPT, Claude, or Copilot.
Testimonials (1)
That i gained a knowledge regarding streamlit library from python and for sure i'll try to use it to improve applications in my team which are made in R shiny
